Other

# A tibble: 6 × 4
  anxiety flexibility mindfulness activity
    <dbl>       <dbl> <chr>       <chr>   
1    50.2        44.4 no          pilates 
2    31.5        47.7 yes         altro   
3    34.0        65.6 no          pilates 
4    37.1        50.7 no          altro   
5    48.3        51.3 no          altro   
6    16.9        67.2 yes         altro   

see this specific code

head(data)

# A tibble: 6 × 4
  anxiety flexibility mindfulness activity
    <dbl>       <dbl> <chr>       <chr>   
1    50.2        44.4 no          pilates 
2    31.5        47.7 yes         altro   
3    34.0        65.6 no          pilates 
4    37.1        50.7 no          altro   
5    48.3        51.3 no          altro   
6    16.9        67.2 yes         altro   

see this specific code

```{r}
#| code-fold: true
#| code-summary: see this specific code
head(data)
```

# A tibble: 6 × 4
  anxiety flexibility mindfulness activity
    <dbl>       <dbl> <chr>       <chr>   
1    50.2        44.4 no          pilates 
2    31.5        47.7 yes         altro   
3    34.0        65.6 no          pilates 
4    37.1        50.7 no          altro   
5    48.3        51.3 no          altro   
6    16.9        67.2 yes         altro   

see this specific code

```{r}
#| code-fold: true
#| code-summary: see this specific code
str(data)
```

spc_tbl_ [300 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ anxiety    : num [1:300] 50.2 31.5 34 37.1 48.3 ...
 $ flexibility: num [1:300] 44.4 47.7 65.6 50.7 51.3 ...
 $ mindfulness: chr [1:300] "no" "yes" "no" "no" ...
 $ activity   : chr [1:300] "pilates" "altro" "pilates" "altro" ...
 - attr(*, "spec")=
  .. cols(
  ..   anxiety = col_double(),
  ..   flexibility = col_double(),
  ..   mindfulness = col_character(),
  ..   activity = col_character()
  .. )
 - attr(*, "problems")=<externalptr> 

see this specific code

summary(data)

    anxiety        flexibility    mindfulness          activity        
 Min.   : 1.483   Min.   :26.91   Length:300         Length:300        
 1st Qu.:25.977   1st Qu.:44.24   Class :character   Class :character  
 Median :33.922   Median :49.56   Mode  :character   Mode  :character  
 Mean   :33.418   Mean   :50.34                                        
 3rd Qu.:41.974   3rd Qu.:56.32                                        
 Max.   :64.775   Max.   :82.41                                        

see this specific code

```{r}
#| code-fold: true
#| code-summary: see this specific code
summary(data)
```

    anxiety        flexibility    mindfulness          activity        
 Min.   : 1.483   Min.   :26.91   Length:300         Length:300        
 1st Qu.:25.977   1st Qu.:44.24   Class :character   Class :character  
 Median :33.922   Median :49.56   Mode  :character   Mode  :character  
 Mean   :33.418   Mean   :50.34                                        
 3rd Qu.:41.974   3rd Qu.:56.32                                        
 Max.   :64.775   Max.   :82.41                                        

Table of the data for anxiety and flexibility

Table 1 illustrates the characteristics of the population of interest

knitr::kable(head(data))

Table 1: Characteristics of population

anxiety	flexibility	mindfulness	activity
50.17244	44.39524	no	pilates
31.47451	47.69823	yes	altro
34.03981	65.58708	no	pilates
37.06712	50.70508	no	altro
48.30549	51.29288	no	altro
16.94087	67.15065	yes	altro

?@lst-table1 illustrates a basic use of the kable() function

Correlation between anxiety and flexibility stratified based on mindfulness

?@lst-plot1 illustrates a basic use of the function plot()

see this specific code

ggplot(data,
       aes(x=flexibility, y=anxiety, color=mindfulness)) +
  geom_point()

Figure 1: Correlation between anxiety and flexibility stratified based on mindfulness

Figure and table

ggplot(mtcars, 
       aes(hp, mpg, color = factor(am))) +
  geom_point() +
  geom_smooth(formula = y ~ x, method = "loess") +
  theme(legend.position = 'bottom')

datatable(mtcars,
  options = list(pageLength = 5))

knitr::kable(head(mtcars))

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.460	20.22	1	0	3	1

ggplot(mtcars, 
       aes(hp, mpg, color = factor(am))) +
  geom_point() +
  geom_smooth(formula = y ~ x, method = "loess") +
  theme(legend.position = 'bottom')

Multiple plots Figure 2 illustrates different things. Figure 2 (a) illustrates the correlation between anxiety and flexibility, Figure 2 (b) illustrates the polynomial regression model, Figure 2 (c) the Linear regression model, Figure 2 (d) the GLM with Poisson

(a) The data

(b) Polynomial

(c) Linear Model

(d) GLM with Poisson

Figure 2: One dataset, different models

Table 2 presents two datasets: Table 2 (a) is cars and Table 2 (b) is pressure

Table 2: Datasets

(a) Cars

speed	dist
4	2
4	10
7	4
7	22
8	16
9	10

(b) Pressure

temperature	pressure
0	0.0002
20	0.0012
40	0.0060
60	0.0300
80	0.0900
100	0.2700

#Annotation

mtcars %>%
  ggplot( aes(mpg, hp, size = gear)) +
  geom_point() +
  geom_smooth(method = "lm")

1

This does that

2

This other thing is this

3

And look at this!

4

Please have mercy

`geom_smooth()` using formula = 'y ~ x'

The mean of the gear variable in mtcars is mean(mtcars$gear)

The mean of the gear variable in mtcars is 3.6875